Artificial Intelligence as a Reflective Catalyst: EFL Lecturers' Transformative Learning and Identity Negotiation in Higher Education
(1) Universitas Prima Indonesia
(2) Universitas Prima Indonesia
(3) Universitas Sam Ratulangi
(4) Universitas Simalungun
(5) Universitas Simalungun
(*) Corresponding Author
Abstract
EFL lecturers face a critical challenge: AI tools are reshaping classroom authority and professional identity faster than institutions can support teacher adaptation. This study examines how AI tools function as reflective catalysts that trigger transformative learning among EFL lecturers in Indonesian higher education. Using narrative inquiry, ten EFL lecturers from five universities in North Sumatra participated in a sixteen-week study during 2024. Data were gathered through three rounds of semi-structured interviews, weekly reflective journals, and classroom observations. Participants, with teaching experience ranging from 5 to 18 years, regularly used ChatGPT, Grammarly, and Google Translate. Unlike prior studies that position teachers as mere implementers of technology, this study foregrounds teacher identity and agency as central to AI integration outcomes. The findings reveal four patterns: AI consistently disrupted routine practices, most visibly in assessment and feedback, reducing average teacher talk time from 65% to 40% and prompting seven lecturers to shift toward inquiry-based learning. Reflective episodes emerged most sharply when AI feedback contradicted teacher judgments, producing three identity responses: empowerment, vulnerability, and identity reconstruction. These findings extend Mezirow's transformative learning theory into real-time, technology-mediated classroom practice and demonstrate that successful AI integration requires institutional support for teacher identity negotiation, rather than solely technical training.
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DOI: https://doi.org/10.26714/lensa.16.1.2026.1-17
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